{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 有哪些跨市场套利指标？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 目录\n",
    "1. Relative Strength\n",
    "2. Bollinger Band Divergence\n",
    "3. Intermarket Disparity\n",
    "4. Intermarket LRS Divergence\n",
    "5. Intermarket Regression Divergence\n",
    "6. Intermarket Momentum Oscillator\n",
    "7. Z-Score Divergence\n",
    "8. Multiple Intermarket Divergence\n",
    "9. Multiple regression Divergence\n",
    "10. Congestion Index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relative Strength\n",
    "$SEC_1/SEC_2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            SEC_1  SEC_2        RS\n",
      "date                              \n",
      "2017-05-11   4.93   3.41  1.445748\n",
      "2017-05-12   5.06   3.46  1.462428\n",
      "2017-05-15   5.10   3.44  1.482558\n",
      "2017-05-16   5.00   3.41  1.466276\n",
      "2017-05-17   4.96   3.41  1.454545\n"
     ]
    },
    {
     "data": {
      "image/png": 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2e+gb5izM757renamSycKm+fswZ43w3lbcCzcFNQBAq2riAryITJIjEBOfXk1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\nnLtCoVAEIP8PmOKkJ1IZveoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa1e2d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import tushare as ts\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "SEC_1 = ts.get_k_data('601398', start='2015-01-01', ktype='D', autype='qfq')\n",
    "SEC_1.index = pd.to_datetime(SEC_1['date'], format='%Y-%m-%d')\n",
    "\n",
    "SEC_2 = ts.get_k_data('601288', start='2015-01-01', ktype='D', autype='qfq')\n",
    "SEC_2.index = pd.to_datetime(SEC_2['date'], format='%Y-%m-%d')\n",
    "\n",
    "SEC_df = pd.concat([SEC_1.close, SEC_2.close], axis=1)\n",
    "SEC_df.columns = ['SEC_1', 'SEC_2']\n",
    "\n",
    "SEC_df['RS'] = SEC_df.SEC_1/SEC_df.SEC_2\n",
    "\n",
    "print SEC_df.tail()\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(SEC_df.SEC_1)\n",
    "plt.plot(SEC_df.SEC_2)\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(SEC_df.RS)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bollinger Band Divergence\n",
    "$SEC_1BOL = 1+\\frac{(C-MA+2SD)}{4SD}$\n",
    "\n",
    "$DIVERGENCE = EMA(3)[\\frac{(SEC_2BOL-SEC_1BOL)}{SEC_1BOL}*100]$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+QfUjT6f2D3FGCnRFURQGhvvSJ8ir2z09uoogbw/nsl8vqjn2CTJSXmumzmKj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\nocn4f1YPtHIXPnrkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbe26978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import talib as ta\n",
    "\n",
    "def SD(series, window=10):\n",
    "    return series.rolling(window).std()\n",
    "\n",
    "SEC_df['SEC_1_SD'] = SD(SEC_df.SEC_1)\n",
    "SEC_df['SEC_2_SD'] = SD(SEC_df.SEC_2)\n",
    "SEC_df['SEC_1_MA'] = ta.abstract.MA(SEC_df, 10, price='SEC_1')\n",
    "SEC_df['SEC_2_MA'] = ta.abstract.MA(SEC_df, 10, price='SEC_2')\n",
    "\n",
    "SEC_1_BOL = 1+(SEC_df.SEC_1-SEC_df.SEC_1_MA+2*SEC_df.SEC_1_SD)/4*SEC_df.SEC_1_SD\n",
    "SEC_2_BOL = 1+(SEC_df.SEC_2-SEC_df.SEC_2_MA+2*SEC_df.SEC_2_SD)/4*SEC_df.SEC_2_SD\n",
    "DIV = (SEC_2_BOL-SEC_1_BOL)/SEC_1_BOL*100\n",
    "DIVERGENCE = pd.Series(ta.EMA(DIV.values, 3),index=DIV.index)\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(SEC_df.SEC_1)\n",
    "plt.plot(SEC_df.SEC_2)\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(DIVERGENCE)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intermarket Disparity\n",
    "\n",
    "$DS_1=\\frac {(SEC_1-MA(SEC_1))}{MA(SEC_1)*100}$\n",
    "\n",
    "$DS_2=\\frac {(SEC_2-MA(SEC_2))}{MA(SEC_2)*100}$\n",
    "\n",
    "$Intermarket Disparity (ID)=c*DS_2-DS_1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date\n",
      "2017-05-11    0.263542\n",
      "2017-05-12   -0.780424\n",
      "2017-05-15   -1.907471\n",
      "2017-05-16   -0.626029\n",
      "2017-05-17    0.194465\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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YZFpMz3926QIAYqEkrGuelRplO79y9hxMUYQcsLtiLjhyouvnnSUFACAcYpag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\nBFEIJOwjAPKxEwRRCCTsIwAq2EUQRCGQsI8Agi7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x48b6e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print SEC_df\n",
    "DS_1 = (SEC_df.SEC_1-SEC_df.SEC_1_MA)/SEC_df.SEC_1_MA*100\n",
    "DS_2 = (SEC_df.SEC_2-SEC_df.SEC_2_MA)/SEC_df.SEC_2_MA*100\n",
    "ID = 1*DS_2-DS_1 \n",
    "print ID.tail()\n",
    "plt.plot(ID)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intermarket LRS Divergence\n",
    "$LRS = Slope(SEC_1)-Slope(SEC_2)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date\n",
      "2017-05-11    0.003333\n",
      "2017-05-12    0.007697\n",
      "2017-05-15    0.014121\n",
      "2017-05-16    0.014121\n",
      "2017-05-17    0.011394\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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+8STWfeMpvLRvEGcvbIFfU2wxBpF62d0cwvzWMFrC/qKZVyIP30sODcYwv8Uo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\nxeiVJPoluY0xkDAQxOyChGEWIXRAdEr1uXUlNVJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc308860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slope_SEC_1 = ta.abstract.LINEARREG_SLOPE(SEC_df, 10, price='SEC_1')\n",
    "slope_SEC_2 = ta.abstract.LINEARREG_SLOPE(SEC_df, 10, price='SEC_2')\n",
    "\n",
    "LRS = slope_SEC_1 - slope_SEC_2\n",
    "print LRS.tail()\n",
    "plt.plot(LRS)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Intermarket Regression Divergence\n",
    "$Y(Pred)=r*\\frac{std(Y)}{std(X)}*X+MA(Y)-MA(X)*r*std(Y)/std(X)$\n",
    "\n",
    "$IRD = Y(Pred)-Y(Actual)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6goWoD2ghkRHdBHToZK1uzu+e3Zi0qhFPqyw2b7/XlbKgmlCVheCxmoAUxVzG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc102ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y = SEC_df.SEC_1\n",
    "X = SEC_df.SEC_2\n",
    "\n",
    "SEC_df['r'] = X.rolling(10).corr(Y)\n",
    "\n",
    "Y_Pred = SEC_df.r*SEC_df.SEC_1_SD/SEC_df.SEC_2_SD*X+ SEC_df.SEC_1_MA-SEC_df.SEC_2_MA*SEC_df.r*SEC_df.SEC_1_SD/SEC_df.SEC_2_SD\n",
    "\n",
    "IRD = Y_Pred - Y\n",
    "plt.plot(IRD)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intermarket Momentum Oscillator\n",
    "$\\frac {MA(IRD - lowest(IRD,200))*100, 3)}{MA(highest(IRD, 200)-lowest(IRD, 200))}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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x6ZLnw9II2WeggZm/tSbvKbJT5x+S+fs7kT9183m48ZwR5NIquu0MkE03C6vm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\n7uufv3iyxl+5PHl4GttHcp7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bJkXVoJF1/lGaP6UUByby2BTi48+TVRWnTBNw6/X5pjCN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc183080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def lowest(series, window=200):\n",
    "    return series.rolling(window).min()\n",
    "\n",
    "def highest(series, window=200):\n",
    "    return series.rolling(window).max()\n",
    "\n",
    "n = (IRD - lowest(IRD))*100\n",
    "numerator = pd.Series(ta.MA(n.values, 3), index=n.index)\n",
    "d = highest(IRD)-lowest(IRD)\n",
    "denominator = pd.Series(ta.MA(d.values, 3), index=d.index)\n",
    "\n",
    "IMO = numerator/denominator\n",
    "\n",
    "plt.plot(IMO)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Z-Score Divergence\n",
    "$Z=\\frac{(C-MA)}{SD}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Y59htpBgD7DWMPSCJaQxHkmLctGw3usc7Qv4+VMX6tpBi2Jg1diMBmDh2tRLi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\n1DjKUBv2GjVq1DjK8H8Bw4imRxXXkGQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbffeb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z1 = (SEC_df.SEC_1 - SEC_df.SEC_1_MA)/SEC_df.SEC_1_SD\n",
    "Z2 = (SEC_df.SEC_2 - SEC_df.SEC_2_MA)/SEC_df.SEC_2_SD\n",
    "plt.plot(Z1)\n",
    "plt.plot(Z2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Intermarket Divergence\n",
    "$r_{y1(2)} = \\frac {r_{y1}-r_{y2}r_{12}}{\\sqrt{1-r^2_{12}}}$\n",
    "\n",
    "$r_{y2(1)} = \\frac {r_{y2}-r_{y1}r_{12}}{\\sqrt{1-r^2_{12}}}$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple regression Divergence\n",
    "$Y= a + b_1X_1+b_2X_2+...b_kX_k$\n",
    "\n",
    "用大盘，成交量，SEC_2来回归SEC_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>SEC_1_C</td>     <th>  R-squared:         </th> <td>   0.864</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.863</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1189.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 18 May 2017</td> <th>  Prob (F-statistic):</th> <td>7.16e-243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:23:54</td>     <th>  Log-Likelihood:    </th> <td>  433.06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   566</td>      <th>  AIC:               </th> <td>  -858.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   562</td>      <th>  BIC:               </th> <td>  -840.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.5830</td> <td>    0.094</td> <td>   -6.235</td> <td> 0.000</td> <td>   -0.767</td> <td>   -0.399</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>-4.401e-06</td> <td> 1.53e-05</td> <td>   -0.288</td> <td> 0.774</td> <td>-3.44e-05</td> <td> 2.56e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>-1.755e-08</td> <td> 2.12e-09</td> <td>   -8.279</td> <td> 0.000</td> <td>-2.17e-08</td> <td>-1.34e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>    <td>    1.6045</td> <td>    0.036</td> <td>   44.009</td> <td> 0.000</td> <td>    1.533</td> <td>    1.676</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>13.401</td> <th>  Durbin-Watson:     </th> <td>   0.097</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  22.254</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.144</td> <th>  Prob(JB):          </th> <td>1.47e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.928</td> <th>  Cond. No.          </th> <td>8.56e+07</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                SEC_1_C   R-squared:                       0.864\n",
       "Model:                            OLS   Adj. R-squared:                  0.863\n",
       "Method:                 Least Squares   F-statistic:                     1189.\n",
       "Date:                Thu, 18 May 2017   Prob (F-statistic):          7.16e-243\n",
       "Time:                        09:23:54   Log-Likelihood:                 433.06\n",
       "No. Observations:                 566   AIC:                            -858.1\n",
       "Df Residuals:                     562   BIC:                            -840.8\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.5830      0.094     -6.235      0.000      -0.767      -0.399\n",
       "x1         -4.401e-06   1.53e-05     -0.288      0.774   -3.44e-05    2.56e-05\n",
       "x2         -1.755e-08   2.12e-09     -8.279      0.000   -2.17e-08   -1.34e-08\n",
       "x3             1.6045      0.036     44.009      0.000       1.533       1.676\n",
       "==============================================================================\n",
       "Omnibus:                       13.401   Durbin-Watson:                   0.097\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               22.254\n",
       "Skew:                           0.144   Prob(JB):                     1.47e-05\n",
       "Kurtosis:                       3.928   Cond. No.                     8.56e+07\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 8.56e+07. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs300 = ts.get_hist_data('hs300', start='2015-01-01', ktype='D').close\n",
    "hs300.index = pd.to_datetime(hs300.index, format='%Y-%m-%d')\n",
    "# print SEC_2\n",
    "adv = ta.abstract.MA(SEC_1, 10, price='volume')\n",
    "SEC_1_C = SEC_1.close\n",
    "SEC_2_C = SEC_2.close\n",
    "\n",
    "df = pd.concat([hs300, adv, SEC_1_C, SEC_2_C], axis=1, keys=['hs300', 'adv', 'SEC_1_C', 'SEC_2_C'])\n",
    "df = df.dropna()\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from statsmodels import regression\n",
    "import numpy as np\n",
    "\n",
    "model = regression.linear_model.OLS(df['SEC_1_C'], sm.add_constant(np.column_stack((df['hs300'],\n",
    "                                                                                    df['adv'],\n",
    "                                                                                    df['SEC_2_C']\n",
    "                                                                                    )))).fit()\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>SEC_1_C</td>     <th>  R-squared:         </th> <td>   0.864</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.863</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1787.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 18 May 2017</td> <th>  Prob (F-statistic):</th> <td>1.56e-244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:23:57</td>     <th>  Log-Likelihood:    </th> <td>  433.02</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   566</td>      <th>  AIC:               </th> <td>  -860.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   563</td>      <th>  BIC:               </th> <td>  -847.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.5797</td> <td>    0.093</td> <td>   -6.252</td> <td> 0.000</td> <td>   -0.762</td> <td>   -0.398</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td> -1.78e-08</td> <td> 1.92e-09</td> <td>   -9.260</td> <td> 0.000</td> <td>-2.16e-08</td> <td> -1.4e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>    <td>    1.5987</td> <td>    0.030</td> <td>   52.680</td> <td> 0.000</td> <td>    1.539</td> <td>    1.658</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>13.665</td> <th>  Durbin-Watson:     </th> <td>   0.097</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.001</td> <th>  Jarque-Bera (JB):  </th> <td>  22.479</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.154</td> <th>  Prob(JB):          </th> <td>1.31e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.926</td> <th>  Cond. No.          </th> <td>8.43e+07</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                SEC_1_C   R-squared:                       0.864\n",
       "Model:                            OLS   Adj. R-squared:                  0.863\n",
       "Method:                 Least Squares   F-statistic:                     1787.\n",
       "Date:                Thu, 18 May 2017   Prob (F-statistic):          1.56e-244\n",
       "Time:                        09:23:57   Log-Likelihood:                 433.02\n",
       "No. Observations:                 566   AIC:                            -860.0\n",
       "Df Residuals:                     563   BIC:                            -847.0\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.5797      0.093     -6.252      0.000      -0.762      -0.398\n",
       "x1          -1.78e-08   1.92e-09     -9.260      0.000   -2.16e-08    -1.4e-08\n",
       "x2             1.5987      0.030     52.680      0.000       1.539       1.658\n",
       "==============================================================================\n",
       "Omnibus:                       13.665   Durbin-Watson:                   0.097\n",
       "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               22.479\n",
       "Skew:                           0.154   Prob(JB):                     1.31e-05\n",
       "Kurtosis:                       3.926   Cond. No.                     8.43e+07\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 8.43e+07. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_model = regression.linear_model.OLS(df['SEC_1_C'], sm.add_constant(np.column_stack((\n",
    "                                                                                    df['adv'],\n",
    "                                                                                    df['SEC_2_C']\n",
    "                                                                                    )))).fit()\n",
    "\n",
    "better_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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q+uS/wZPWqK53V5SMHtuZmsWDjs8ASFgyjBeX7OTiIfFM7NcO25H1zAj4hOcW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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc434d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction = better_model.params[0] + better_model.params[1]*df['adv'] + better_model.params[2]*df['SEC_2_C']\n",
    "prediction.name = 'Prediction'\n",
    "divergence = df['SEC_1_C']-prediction\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(df['SEC_1_C'], label='SEC_1')\n",
    "plt.plot(prediction, label='prediction')\n",
    "plt.legend(loc='upper left')\n",
    "plt.subplot(2,1,2)\n",
    "plt.bar(df['SEC_1_C'].index, divergence)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Congestion Index\n",
    "$CI= \\frac {(C-C[X-1])/C[X-1]*100} {(Higest(H,X)-Lowest(L,X))/Lowest(L,X)}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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i8d7mU6LFbbba253deufHuzHs6VVY+N0BbC6qdUqhlM6b9BU0G5YR61e/HYBb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc5ef4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print SEC_1\n",
    "\n",
    "\n",
    "def C_Index(SEC, window=20):\n",
    "    list_CI = []\n",
    "    for i in range(0,len(SEC)):\n",
    "        data_S = SEC.iloc[i:i+window]\n",
    "        C = data_S.close.iloc[-1]\n",
    "        C_W = data_S.close.iloc[0]\n",
    "        H_W = max(data_S.high)\n",
    "        L_W = min(data_S.low)\n",
    "        CI = (C-C_W)/C_W*100/(H_W-L_W)/L_W\n",
    "        list_CI.append(CI)\n",
    "    return list_CI\n",
    "\n",
    "Congestion_Index = C_Index(SEC_1, window=50)\n",
    "CI = pd.Series(Congestion_Index, index=SEC_1.index)\n",
    "fig,(ax,ax1) = plt.subplots(2,1,sharex=True)\n",
    "ax.plot(SEC_1.close)\n",
    "ax1.plot(CI)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
